Schedule Sampling [1]} offers an alternative to teacher forcing [2]} where, during training time, the model has access to the ground truth label of the previous time-step in each new prediction.
During inference, the model uses its predictions as input in the next training step. This may lead to exposure bias because of the discrepancy between training and inference and result in poor model performance.
Schedule sampling takes benefit from teacher forcing while avoiding exposure bias by gradually replacing the ground-truth tokens by the model’s predictions.
Three different decay schedules were proposed by Bengio et al. [1]}: exponential, inverse sigmoid and linear.
While Ren and Zemel [4]} and Xu et al. [5]} used a linear schedule in their training, Oh et al. [6]} and RVOS [7]} adopted a more drastic scheme, using ground truth labels in the first half of the training, and predicted masks in the second half. We have named this second approach as a step schedule, as in the well-known Heaviside step function.
